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Earned Value Management (EVM) is a progress management method in project management. EVM is used as metrics to analyze execution statuses of projects that need to be accomplished within deadlines and limited budgets. By replacing budgets used in project management with energy used in applications, we can apply EVM as metrics to analyze execution statuses of Deadline Energy Constrained (DEC) applications that execute tasks within deadlines and limited energy. DEC applications are often seen in applications that need to consume energy within limits, for example, Wireless Sensor Networks (WSNs), Electric Cars (ECs), and Unmanned Aerial Vehicle (UAV). In this paper, we introduce new possibilities of applying EVM by showing that EVM can be used to analyze execution statuses of and make changes to DEC applications.

In goal-driven self-adaptive systems, a goal model is used as a requirement model and is held by the system even at runtime. At this moment, the self-adaptive system, which can change its behaviour at runtime, will be able to reason over the variability within the goal model. It will then be able to find the best behaviour to deal with environment evolutions. However, the uncertain nature of the requirements engineering concepts in a real dynamic world is not always rightfully defined. In particular, quality requirements runtime changes are almost never considered in the literacy. Specifically, a problem we highlight here is the lack of context consideration in quality constraint approximation. Our purpose is to clearly define this problem and to propose a first solution. In this paper, we introduce a flexible version of the quality constraints. This new kind of quality constraints can be rewritten at runtime to tackle the context change induced by the environment change. To stick the constraint definition to the context change, we developed a new algorithm which modifies the specification of the quality constraints at runtime.

Self-adaptive systems alter their behavior in response to environmental changes to continually satisfy their requirements. Self-adaptive systems employ an environment model, which should be updated during runtime to maintain consistency with the real environment. Although some techniques have been proposed to learn environment model based on execution traces at the design time, these techniques are time consuming and consequently inappropriate for runtime learning. Herein, a technique using a stochastic gradient descent and the difference in the data acquired during the run-time is proposed as an efficient learning environment model. The computational time and accuracy of our technique are verified through a case study.

Wireless sensor networks (WSNs) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. In this paper, we focus on faults occurred due to low battery and calibration in WSNs. Machine learning algorithms have been successfully used to identify and classify various types of faults. In this paper, we evaluate and compare the performance of k-nearest neighbour, support vector machine (SVM), and Naive Bayes machine learning algorithms by using the real-world datasets to identify and classify the faults. We present here a comparative study of the above mentioned approaches on experimental datasets. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab. The results show that the k-nearest neighbour (kNN) algorithm obtained a better fault detection rate than other algorithms based on given performance metrics.

In many application domains, continuous operation is a desirable attribute for software-intensive systems. As the environment or system requirements change, so the system should change and adapt without stopping or unduly disturbing its operation. There is, therefore, a need for sound engineering techniques that can cope with dynamic change. In this paper we address the problem of dynamic update of controllers in reactive systems when the specification (environment assumptions, requirements and interface) of the current system changes. We present a general approach to specifying correctness criteria for dynamic update and a technique for automatically computing a controller that handles the transition from the old to the new specification, assuring that the system will reach a state in which such a transition can correctly occur. Indeed, using controller synthesis we show how to automatically build a controller that guarantees both progress towards update and safe update. Seven case studies have been implemented to validate the approach.

To meet future demands for wireless sensor network (WSN) software, both experts and average software developers should be involved in WSN software development. However, WSN software development is difficult for the average software developer because data processing-related design and network-related design are tangled in the software. Here, we propose a software development process for WSN software by stepwise refinement. Our process enables stepwise refinement to separately address data processing-related and network-related concerns, reuse of well-defined designs, and implementations for network-related concerns prepared by the experts, and perform model-driven development to obtain source codes from models by model transformations. Additionally, we used case studies using actual WSN software development and user studies to evaluate how our proposed process can support actual WSN software development.

To meet future demands for wireless sensor network (WSN) software, both experts and average software developers should be involved in WSN software development. However, WSN software development is difficult for the average software developer because data processing-related design and network-related design are tangled in the software. Here, we propose a software development process for WSN software by stepwise refinement. Our process enables stepwise refinement to separately address data processing-related and network-related concerns, reuse of well-defined designs, and implementations for network-related concerns prepared by the experts, and perform model-driven development to obtain source codes from models by model transformations. Additionally, we used case studies using actual WSN software development and user studies to evaluate how our proposed process can support actual WSN software development.

In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.

無線センサネットワークアプリケーションの機能要求を達成するための主な手段は実世界のデータ取得であるため,開発においてデータに関する品質を改善することが重要となる.開発時には,低コストでプロトタイプを開発し早期に品質改善のための課題を発見すること,細やかな品質改善により要求を満たすことが求められる.しかし,既存の開発方法では単一の抽象度の言語を一貫して用いるため,記述能力と記述の容易さとの間にトレードオフによってこれらの要件を同時に満たせない.本論文では,抽象度の異なる複数のモデリング言語を併用することでデータ品質改善を支援する開発プロセスを提案する.本開発プロセスでは,設計対象ごとに適した抽象度を用いるようモデリング言語を定義し,これらの使い分けをプロセスとして提案する.また,上位モデルの設計を下位モデルへ反映させつつ実装へと近づける際の設計の伝播は,自動モデル変換規則を定義することで実現した.本開発プロセスが前述の要件を満たしつつ開発可能であることを,運用実績のあるWSNアプリケーションでのケーススタディを用い示した.

Home cleaning robots have become popular. Most of the home cleaning robots are based on ground vehicles. While the cleaning robots based on ground vehicles can vacuum or wash floors robustly and efficiently, but they only clean on floors, not on stairs or furnitures. In this demo, we show a new concept of cleaning robot, called HoppingDuster. HoppingDuster is based on an aerial vehicle, which can y to stairs or furnitures to be cleaned, and hop or hover to wipe or blow down dust on them toward the floor so that the ground cleaning robot can vacuum the dust. HoppingDuster adapts its behavior to finish it cleaning within designated time and battery capacity.

Applications in wireless sensor networks (WSNs) sometimes cannot deliver the quality required by users due to packet loss and node leaving. There is a trade-off between energy and quality: increasing quality requires energy, and decreasing energy invites a lack of quality. To adjust the trade-off, actions which make changes to the energy and quality condition are required. There has been previous research on actions to reduce energy and to improve quality, but how to select actions and methods in accordance with the condition has not been established. To overcome this problem, we propose a framework that can select suitable actions from multiple choices based on an analysis of the condition by applying methods found in Project Management. This framework consists of (1) Earned Value Management (EVM) and (2) an Action Table. EVM is a metric that is used to analyze cost and progress, and the Action Table is a method to select appropriate actions in accordance with condition. Simulation results show that the proposed framework can adjust the trade-off more effectively than conventional frameworks by selecting and changing the most suitable actions.

Wireless sensor networks (WSNs) play important roles in systems that interact with the real world (e.g., cyber-physical systems and internet of things). To mitigate the complex nature of WSNs, many platforms at different abstraction levels (e.g., abstraction of hardware, communications, and individual nodes) have been proposed in the last decade. WSNs in different environments might employ different platforms to satisfy nonfunctional requirements (NFRs) because the diversity of platforms results in trade-offs of NFRs and the quality of WSN heavily depends on the environment it runs. Although WSN software should be highly portable, existing approaches only support porting between operating systems and not more abstract middleware. Herein we propose a methodology with a framework to capture various platforms in the WSN domain with platform-independent multi-level models. A case study demonstrates that our framework can port WSN software to platforms at different abstraction levels and adapt the software to the new platform to improve performance.

Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab. © 2013 IEEE.

In pervasive computing environments, wireless sensor networks play an important infrastructure role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time in a decentralised manner
however, sensed data is often faulty. We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection. A combination of neighbourhood voting and time series data analysis techniques are used to detect faults. We also study the comparative accuracy of both the union and the intersection of the two techniques. Then, detected faults are classified into known fault categories. An initial evaluation with SensorScope, an outdoor temperature dataset, confirms that our solution is able to detect and classify faulty readings into four fault types, namely, 1) random, 2) malfunction, 3) bias, and 4) drift with accuracy up to 95%. The results also show that, with the experimental dataset, the time series data analysis technique performs comparable well in most of the cases, whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types. Copyright © 2013 ACM.

In systems that require several services to collaborate, specifying coordination protocols is vital, but costly. Additionally, several properties, which are derived from laws, regulations, requirements, etc., must be satisfied. Coordination protocol composition approaches construct specific protocols in a cost effective manner in accordance with the composition intentions. However, existing composition approaches are insufficient in terms of satisfying properties. Existing approaches use concrete specifications to identify composition intentions and do not consider interference between compositions. Herein we propose a new composition approach in which a developer directly expresses his or her intentions as constraints via metadata, and then the system searches for optimal composition methods based on the constraints. © 2013 IEEE.

Primary task of wireless sensor networks is to deliver reliable and accurate information about the phenomena of interest. However, faults are a frequent occurrence and their accumulation affects the quality of service significantly. This leads to a shorter effective lifetime of the network. In this work, we propose a framework for the fault tolerance in sensory readings. The main concept is based on the observation of the pattern that faults leave in data behavior. Based on the duration, continuity and the impact, we propose a complete and consistent classification of faults as they can be observed in sensory readings independently of the underlying cause. Further, we propose that network learns a model of a fault for each faulty node from the past behavior. Each phase of the framework can be implemented with the use of different algorithms appropriate for the task. In this paper we present an instance that relies on neighborhood vote, time series analysis and statistical pattern recognition. Results so far confirm that the scheme works well for dense data-centric wireless sensor networks.

Best Paper Award

This paper introduces a declarative agent-oriented language for Ambient Intelligence - S-CLAIM - that allows programming reactive or cognitive mobile agents in a simple, easy-to-use manner while meeting AmI requirements. Based on a hierarchical representation of the agents, the language offers a natural solution to achieve context-sensitivity. S-CLAIM is light-weight and, being transparently underpinned by the JADE framework, allows deployment on mobile devices and easy interoperation with other components by means of web services. The usefulness of the proposed language for AmI is illustrated through a scenario and a demo featuring an AmI application in a Smart Room. (C) 2011 Published by Elsevier Ltd.

Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.

Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. We focus on identifying data fault types and their causes. In particular, we propose a hybrid approach to the detection of faults based on three qualitatively different classes of fault detection methods. Rule-based methods leverage domain and expert knowledge to develop heuristic rules for identifying and classifying faults. Estimation methods predict normal sensor behavior by leveraging sensor spatial and temporal correlations, identifying erroneous sensor readings as faults. Finally, learning-based methods are inferred a model for the faulty sensor readings using training data and statistically detect and identify classes of faults. We illustrate the performance of a hybrid approach on data coming from two actual sensor deployments.

This paper introduces a declarative agent-oriented language for Ambient Intelligence - S-CLAIM - that allows programming reactive or cognitive mobile agents in a simple, easy-to-use manner while meeting AmI requirements. Based on a hierarchical representation of the agents, the language offers a natural solution to achieve context-sensitivity. S-CLAIM is light-weight and, being transparently underpinned by the JADE framework, allows deployment on mobile devices and easy interoperation with other components by means of web services. The usefulness of the proposed language for AmI is illustrated through a scenario and a demo featuring an AmI application in a Smart Room. (C) 2011 Published by Elsevier Ltd.

Model-driven development (MDD) facilitates the development of wireless sensor network (WSN) applications because MDD can deal with models at the abstraction level to the concrete level. In the MDD for WSN applications, the developers model the dataflow at the abstract level and model the designs of a communication and task assignment at the concrete level. In our previous work we proposed a MDD process for WSN applications with three domain specific languages (DSLs). However, we did not sufficiently evaluate the description capabilities of our DSLs. For that reason, we report case studies on real-world applications to prove the feasibility of our approach. Our DSL can be used to describe both the application logic and design decisions concerning the quality of data, which are adopted in target applications. Additionally, we demonstrate the effectiveness of the DSLs at multiple abstraction levels. © 2012 IEEE.

Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. In this preliminary work, we propose a hybrid approach to the detection of faults and we illustrate its performance on data coming from a real sensor deployment. The proposal is a first step to have a hybrid method towards automated on-line fault detection and classification in context-aware WSNs middleware framework.

無線センサネットワーク上で動作する移動体追跡アプリケーションには,期待する分解能を満たしつつ,データ送信回数を抑制することが求められる.この問題に対し,ソフトウェアによってセンサの計測値をフィルタし,分解能を抑えることでデータ送信回数を抑制するセンサモデルが提案されている.しかし,既存のセンサモデルでは分解能の調整が行えず,分解能とデータ送信回数の細かなトレードオフ調整が困難であった.そこで本論文では.(1)分解能とデータ送信回数のトレードオフを調整可能にするN-aryセンサモデルと,(2)ノードの配置状況に応じて地域ごとに得られる分解能を均一化するN-aryセンサモデル設定手法を提案する.更に,シミュレーション結果により,本手法を用いることで,システムに期待する分解能要求に合わせてシステムの分解能を調整し,過分な通信を抑えることが可能となることを示した.

Pervasive applications incorporate physical components that are exposed to everyday use and a large number of conditions and external factors that can lead to faults and failures. It is also possible that application requirements change during deployment and the network needs to adapt to a new context. Consequently, pervasive systems must be capable to autonomically adapt to changing conditions without involving users becoming a transparent asset in the environment. In this paper, we present an autonomic mechanism for initial task assignment in sensor networks, an NP-hard problem. We also study on-line adaptation of the original deployment which considers real-time metrics for maximising utility and lifetime of applications and smooth service degradation in the face of component failures. © 2011 IEEE.

In order to develop Wireless Sensor Network (WSN) applications, it is necessary to develop prototypes in a low-cost way and to optimize application performance. Existing development approaches enable to develop a low-cost prototype by concealing the detail of WSN from the developers. However, there is a trade-off between the development cost of prototype and the description capability needed to optimize the application performance. We propose a Model-Driven Development (MDD) process to enable a low-cost prototyping and detailed optimization. To enable such a development process, we define modeling languages, which describe an application at three abstraction levels, and transformation rules, which transform models described by our modeling language to concrete one. Using our process, in prototyping, the developer describes a model by using the modeling language at the highest abstraction level and automatically obtains an executable model by using transformation rules. In addition, in optimizing, the developer can automatically obtain the models at more concrete abstraction level than the prototype by using transformation rules and modifies them in greater detail by using each modeling language. © 2011 ACM.

マルチエージェントシステムではエージェント間の協調動作を定義する協調プロトコルが重要である.しかし,多数のエージェントが参加する協調プロトコルを定義することは非常に複雑でコストの高いタスクである.複雑な協調プロトコルを容易に作成する手法として協調プロトコル合成があるが,既存の協調プロトコル合成では一つの協調プロトコルを複数の箇所に合成する場合には,合成のコストを十分に低減することができなかった.本論文では協調プロトコル合成の新しい手法として,メタデータを用いた協調プロトコル合成手法を提案する.本手法を用いることで,一つの協調プロトコルを複数箇所に合成する場合の合成のコストを低減することができる.

エージェントは,環境変化に対応して適切な動作を選択,実行する適応能力を備えるソフトウェアとして,実環境への適用が期待されている.このような適応能力を備えたエージェントを実環境上で実行させる場合,実環境の状態を把握し,さらに実環境に影響を与える動作を実行するためのシステムが必要となる.一方で,ユビキタスコンピューティングの分野では,実環境への入出力に関する研究が活発に行われ,様々なミドルウェアが提案されている.本論文では,ユビキタスコンピューティング向けのミドルウェアを紹介し,適応能力を備えたエージェントとの連携についての可能性や課題について論じる.

Multi-agent systems need protocols to coordinate among agents implemented by different owners. However specifying coordination protocols for many participating agents is a complex task. A protocol composition approach, which can reduce the complexity of specifying such a coordination protocol, must specify how to compose coordination protocols in a composition configuration. Current protocol compositions cannot sufficiently reduce the complexity because composing a protocol several times requires the specification of too many configurations. We propose a protocol composition approach that can specify composition configurations in an abstract way. We assign metadata to the messages in a coordination protocol and use the metadata to specify the configuration. An abstract configuration using metadata can be applied to various protocol compositions and can reduce the number of specifications required for configuration composition.

In e-Business, agents need to coordinate with each other Coordination protocols that specify the defining orders of message passing are very important. The scale of e-Business grows with the advancement of technology, and the number of agents involved continues to increase. Specifying the coordination protocols for so many participating agents is a complex task. A coordination protocol composition approach reduces the complexity of specifying such a coordination protocol. It treats coordination protocols as individual parts and composes them to construct the intended protocols. However existing approaches do not sufficiently reduce the complexity when a coordination protocol is composed several times, because too many configurations are required to specify a composition. A new approach is proposed that uses metadata to specify the compositions by specifying only one configuration. It can reduce the number of configurations when a coordination protocol is composed several times.

In a sensor network, the technique that limits the number of sensors used for observation is effective to reduce the energy consumption of each sensor. To limit the number of sensors without sacrificing observation accuracy, an appropriate sensor combination must be selected by evaluating the observation effectiveness of various combinations. However, the computational workload for evaluating all the sensor combinations is quite large. We can define a parameter related to the optimal size of a region around an observation target by making a trade-off between accuracy and the computational workload. In region-based sensor selection, a combination of sensors is selected from that is near the observation target. Accuracy is better in a larger region with a lot of sensors, but the computational workload is heavier. In contrast, a smaller region with fewer sensors has poorer accuracy, but a lighter workload. The size of the region controls the trade-off between accuracy and the computational workload. We define a parameter related to the optimal size of a region, and use it to dynamically adjust the region's size. Our simulations confirmed that region-based sensor selection reduces the computational workload and improves accuracy in comparison to existing techniques. © 2008 IEEE.

Wireless sensor networks (WSNs) have attracted attention in recent years. Many sensors will be equipped in buildings and provide data about the context of buildings in the future. We can provide many kinds of services that use various context information acquired from wireless sensor networks. However, sensing programs may not be able to be executed concurrently on the same sensor node because of sensors' resource restrictions. So, sensing programs sometimes have to relocate to other sensors if necessary. In this paper, we suggest a middleware for the relocation of a sensing program. Considering severe resource restrictions in a wireless sensor network, we can make a sensing program with a small code footprint. © 2006 IEEE.

Prolonging a network lifetime is one of crucial issues of message routing protocol in mobile ad-hoc networks (MANETs), because MANETs consist of mobile devices powered by batteries. Previous routing protocols focus on route selection by a message sender to balance message forwarding load. However, in this approach, message routes determined by different senders cannot be coordinated, because each sender selects its message route independently. In this paper, we propose an auction-based route allocation scheme where message routes are determined by auctions between message senders and forwarders, and apply the route allocation scheme to the Dynamic Source Routing protocol. Simulation results show the DSR protocol with our allocation scheme can prolong the network lifetime comparing with the routing protocol proposed in the previous works. © 2006 IEEE.

Middleware for wireless sensor network (WSN) abstracts a network as an entity and hides programming difficulties from programmers. Many middlewares have been proposed, but they use different programming languages to manipulate functions in WSNs. This inhibits usability when manipulating multiple WSNs managed by different middlewares, because the primitives of each language have different descriptive capabilities. In this paper, we propose and apply design patterns in WSN programming to complement the capabilities of language primitives, and discuss the effectiveness of these design patterns. First, we discuss major middleware languages and compare the capabilities of their primitives. Second, we extract design patterns from the representative middlewares to cover the missing capabilities identified in the comparison. Finally, we discuss the effectiveness of design patterns for WSN programming. The discussion indicates that design patterns improve the usability of manipulating multiple WSNs.

モバイルアドホックネットワーク上において,省電力性を考慮した位置依存情報収集手法を提案する.遠距離の情報収集地域に関する位置依存情報を収集する場合,従来のように情報収集者端末から,情報収集地域内の端未群と通信しデータを収集するアプローチでは,使用するネットワーク経路が長くなるため,通信による総消費電力量は大きくなる.データ解析プログラムを情報収集地域内の端末に配備し,近距離でデータ収集させ,必要な解析結果だけを取得することで,帯域使用量を節約し,総消費電力量が低減できることが期待できる.しかし,すべての端末は独立的に移動しているため,データ解析プログラムを配備した端末が情報収集地域から離れると,データ収集コストが増大する.データ解析プログラムを処理状態とともに情報収集地域内の他の端末に配備し,データ解析を引き継がせることにより,データ収集コストの増大を防ぐことができるが,一方で,データ解析プログラムの再配備による総消費電力量が増大してしまう.本論文では,データ収集コストと再配備コストのトレードオフを考慮した,データ解析プログラムの再配備手法を提案する.本手法を用いることにより,遠距離地域の位置依存情報を少ない消費電力で収集することが可能となる.

With the spread of mobile phones, the use of Mobile Adhoc NETworks (MANETs) for disaster recovery finally becomes feasible. Information retrieval from the catastrophic place is attended in an energy-efficient manner using the Geographically Bound Mobile Agent (GBMA) model. The GBMA, which is a mobile agent on MANETs that retrieves geographically bound data, migrates to remain in a designated region to maintain low energy consumption for data retrieval, and provides location based migration scheme to eliminate needless migration to reduce energy consumption. In the data retrieval using the GBMA model, survivability of the agent is important. In a MANET, a GBMA with retrieved data may be lost due to its host's death. The lost of the agent causes re-execution of the retrieval process, which depraves energy efficiency. We propose migration strategies of the GBMA to improve its survivability. In the migration strategies, the selection of the next host node is parameterized by node location, speed, connectivity, and battery level. Moreover, in the strategies, multiple migration trigger policies are defined to escape from a dying node. We present the implementation of migration strategies and confirm the achievements with several simulations. This finally leads to the adaptive Geographically Bound Mobile Agent model, which consumes even less energy.

近年、センサーネットワークが注目されており、将来的に建物内などではセンサーが完備された環境が実現されると予想される。このセンサーネットワークを利用することでセンサーから取得できる様々なコンテキスト情報を利用したサービスを実現することができる。しかし、センサーは計算資源が少ないなど様々な問題があり、全てのサービスからの要求に対応することは困難となる。そこで、本研究ではセンサーの空き容量やサービスの特徴を考慮した上で、センシングプログラムの退避をサポートするセンサーミドルウェアであるSemiFloAを提案する。

A location-specific data retrieval, which is data retrieval from nodes in a designated region at the time, is an attractive application in a Mobile Ad-hon Network (MANET). However, almost all nodes in a MANET are powered by batteries, the location-specific data retrieval should involve a small number of messages. In this paper, we use a mobile agent to retrieve the location-specific data. A mobile agent migrates to a node in a designated region, and retrieves data from nodes in this region. Since, after migration, the agent can communicate with nodes in the designated region through low overhead short length hops, the mobile agent can retrieve data at low message cost for long periods, even if the owner of this agent moves around. However, even after migrating to node in the designated region, in order to stay near this region, a mobile agent should migrate to other nodes in response to the movement of the node hosting this agent. In this paper, we propose the Geographically Bound Mobile Agent (GBMA) which is a mobile agent that periodically migrates in order to always be located in a designated region. In order to clarify where the GBMA should be located and when the GBMA starts to migrate, two geographic zones are set to the GBMA: required zone and expected zone. The required zone ease tracking of the GBMA, and the expected zone ease adjustment of the GBMA migration timing.

Location-specific data retrieval is an attractive application in a Mobile Ad-hoc Network (MANET). Simple solution for it is that an observer retrieves the data by geocasting from an observer node, but its overhead highly depends on location of the observer and the designated region. We propose a mobile agent approach. A mobile agent migrates from the observer node to a node in the designated region, retrieves data from there, and summarizes, filters, and compresses the retrieved data, This data is sent back to the observer, when the observer request. Since the data is retrieved by the mobile agent located near the data Sources, the data retrieval in the mobile agent approach would involve low overhead, even if the observer is far from the target region or moves around. In the MANET, however, even after the first migration; to stay near data Sources, a mobile agent should migrate to another node in response to node movements.. In this paper, we propose the Geographically Bound Mobile Agent (GBMA) which is a mobile agent that migrates to always be located in a designated region. Moreover, to clarify where the GBMA should be located and when the GBMA starts to migrate, we introduce two geographic zones: required zone and expected zone, Compared with the conventional methods with geocast or with a conventional mobile agent, the GBMA with these zones for retrieving location-specific data can reduce the total number of messages.