Context-Aware Middleware for Pervasive and Mobile Ad Hoc Environments

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1 Context-Aware Middleware for Pervasive and Mobile Ad Hoc Environments Hector A. Duran-Limon Department of Computing Science, Tecnológico de Monterrey (ITESM), Campus Guadalajara, México. Gordon S. Blair, Adrian Friday, Thirunavukkarasu Sivaharan, Maomao Wu, Paul Okanda, Carl-Fredrik Sørensen Computing Department, Lancaster University, Bailrigg, Lancaster LA1 4YR, UK Abstract: Recent advances in the area of mobile ad hoc computing and pervasive computing have driven the emergence of new challenges. For example, the Intelligent Environment or Smart Environment has become one of the key research areas in the pervasive computing arena. Mobile ad hoc scenarios also include time-critical applications such as an autonomous vehicle system in which vehicles are able to operate independently and cooperate with each other to avoid collisions. In this paper, we present a context-aware middleware architecture for the support of both pervasive and mobile ad hoc environments. Keywords: mobile ad hoc networks, pervasive systems, real-time systems, middleware 1. Introduction Recent years have witnessed advances in the enabling technologies for mobile computing, such as the increasingly mature end-systems, various kinds of wireless communication protocols, and mobile networking technologies. As a result new challenges have emerged in both the pervasive and mobile ad hoc computing arenas. Regarding the former, one of the key research areas in recent years is the Intelligent Environment or Smart Environment. A personalized intelligent room, for example, can set the room temperature according to the occupier s preference, play his beloved music, and make his favourite coffee, etc. In addition, examples of mobile ad hoc applications include air traffic control systems whereby thousands of aircraft are proactively coordinated to keep them at safe distance from each other, direct them during takeoff and landing from airports, and ensure that traffic congestions are avoided. Another example is an autonomous vehicle system in which vehicles are able to operate independently and cooperate with each other to avoid collisions. This kind of systems is time-critical and needs to be provided with both timeliness assurances and adaptation mechanisms that lead the system to a safe state in case of unexpected changes introduced in the environment. This paper provides a solution to three important issues: a) support for sentience and intelligent behaviour, b) support for high mobility and pervasiveness, and c) realtime support for mobile ad hoc environments. These challenges are addressed in an integrated manner by our middleware architecture as shown below. We are not aware of any work addressing the three issues altogether. The paper is structured as follows. Section 2 introduces the main challenges of the targeted applications. Relevant related work is mentioned in section 3. Section 4 then presents the overall middleware architecture. Section 5 shows how the challenges are

2 addressed by our middleware architecture. Finally, some concluding remarks are provided in section Challenges 2.1 Challenge 1: Provide intelligent behaviour to pervasive systems In order to make pervasive computing systems intelligent, it is crucial for them to first be sentient they should be able to sense the changes in the environment and make autonomous decisions on how to react. For example, a smart room should be able to obtain the room temperature, the noise level, the light intensity, how many persons are in the room, etc. Moreover, they should also have the capability to process and analyse the sensory data to deduce high-level context and infer long-term personal preferences of the users. By utilising the contextual information and personal preferences, the pervasive computing environments can make autonomous decision according to their own control logic. This decision making process might need certain degree of intelligence, and it might also need to resolve conflicts in the pervasive computing environment. For example, if there are more than two persons in a smart room, the system should be able to decide what temperature to set to minimize the disturbance, what kind of music to play to maximize satisfaction, etc. 2.2 Challenge 2: Provide support for high mobility and pervasiveness We envisage hundreds and even thousands of autonomous entities being involved in the targeted applications. Therefore, suitable communication models are required to handle a many-to-many type of communication. The communication service should be reliable and efficient, i.e. the amount of resources used by the communication protocol should be maintained at acceptable levels. These systems should also be scalable due to the fact that the number of participants may increase considerably over time. Furthermore, the level of mobility used by the system should be considered as this can have an impact on the communication system. For instance, research has shown that communication protocols used in low mobility conditions are not optimal for high mobility scenarios [1]. In addition, mobile ad hoc networks are characterised by being highly unpredictable. Communication delays between nodes may vary unexpectedly as the number of hops to reach the destination changes. Moreover, a geographical area may unexpectedly become congested, resulting in lack of communication resources. Periods of disconnection are also likely to happen at any time due to the conditions of the geographical area. 2.3 Challenge 3: Provide QoS guarantees in mobile ad hoc environments In the current state-of-the-art in mobile ad hoc network technologies, it is not feasible to offer hard real-time guarantees for communication resources except in special cases. Such guarantees can only be offered if certain specific conditions are met such as a scenario whereby a limited number of nodes are moving in an obstaclefree area at the same speed and direction. Although, hard real-time guarantees can still be provided for local resources such as CPU, only soft real-time guarantees can be offered for communication resources in most cases whereby these resources are dynamically allocated according to deadlines. Another tough challenge arises here: how can we deal with applications demanding hard real-time guarantees when the underlying infrastructure is only capable of offering soft real-time guarantees.

3 3. Related work The Gaia project [2] developed at the University of Illinois is a distributed middleware infrastructure that provides support for ubiquitous computing. Although Gaia shares several common design goals with CORTEX, Gaia s main intended application domain is confined to fixed intelligent environments and lacks the support for real-time mobile ad hoc application scenarios. The EasyLiving project [3] from Microsoft focuses on development of architectures and technologies for intelligent environments. It identifies several research efforts required on a variety of fronts, including middleware, geometric world modelling, sensing capabilities, and service description. The middleware, called InConcert, identifies the importance of having an asynchronous communication model for the coordination of entities contained in the environment. However, this approach does not fully address the concerns of real-time mobile ad hoc environments and pervasive application scenarios. Regarding multicast in mobile ad hoc networks, previous research [1] has recognised that most existing algorithms perform inadequately when high mobility is present in the environment. The main reason for these protocols to fail is that these protocols maintain shared state in the nodes in the form of routes and adjacent information, which are rapidly outdated due to high node mobility. In contrast, our multicast protocol is based on a probabilistic flooding algorithm with damping, which does not maintain shared state in nodes. Finally, to the best of our knowledge, no previous work presents an integrated solution to the areas of autonomous intelligent behaviour, pervasive mobile systems and real-time mobile ad hoc computing. 4. Overall Middleware Architecture Central to the CORTEX architecture is the notion of a sentient object [4-6], which is defined as an entity that is able to both consume and produce events. That is, sentient objects are entities that receive events, process them and generate further events. Input events are received from either sensors or other sentient objects (local or remote). Similarly, output events are sent either to actuators or other sentient objects (local or remote). Sentient objects are autonomous entities that are able to sense their environment. Interestingly, sentient objects have a proactive role in that they are capable of making decisions and performing some actions (i.e. generate further events) based on the information sensed. Hence, sentient objects include a control logic which realises the decision-making mechanism. The environment support for the interaction of sentient objects is also conceptualised as a componentised middleware platform. In fact, the middleware is structured in terms of component frameworks (CFs) [7]. Essentially, component frameworks are collections of rules and interfaces that govern the interaction of components plugged into them [7]. In other words, a component framework is a reusable architectural design targeting a specific domain whereby desired architectural properties and invariants are enforced. The publish/subscribe CF realises the CORTEX event model [8]. The functionality of the control engine of a sentient object is provided by the context CF. Facilities for multicast in ad hoc environments

4 are then provided by the multicast CF. The QoS management CF arbitrates the allocation of resources and provides facilities for monitoring and adaptation of QoS. Lastly, the resource management CF controls the resources used by all the CFs [9]. The following section shows how the challenges outlined in section 2 are addressed by our middleware architecture. 5. Approach to Challenges 5.1 Support for Autonomous Intelligent Behaviour The autonomous intelligent behaviour is achieved by the sentient objects. A sentient object has been defined as an entity that both consumes and produces software events, and lies in some control path between at least one sensor and actuator [9]. The internal architecture of a sentient object is realised by the context CF, which consists of four components: sensory capture, context management, inference engine and the learning engine. Getting contextual information from raw sensory data is the main task of context acquisition, and the major issues in the area of sensory capture are data filtering and sensor fusion. The context management component deals with the representation of context information. The inference engine component is actually the brain of a sentient object, and it has some form of a decision-making ability and intelligence. In order to make decisions, some rules need to be applied on the current context. The learning engine component is complementary to the inference engine component, and it can dynamically plug-in different machine learning algorithms that make the system able to learn rules from previous context states and from interaction with the user. The learned rules can either be injected into the inference engine or used by the learning component itself to make decisions. 5.2 Support for High Mobility and Pervasiveness We believe the publish/subscribe or event based communication paradigm is well suited to address the issues of pervasive systems in a mobile ad hoc environment. Moreover, the publish/subscribe model is well recognised to support anonymous and asynchronous communication requirements [10-12]. Hence, many-to-many communication scenarios are well supported by the anonymous dissemination of information. In addition, asynchronous communication is ideal in systems where frequent disconnections are likely to happen thus to avoid blocking conditions. We have adopted the event model STEAM [8] as it addresses a number of core issues with regard to publish/subscribe systems in mobile ad hoc networks. Briefly, an implicit event model for ad hoc networks is defined in which there is not an event broker or mediator, instead brokering functions are implemented at both the consumer and the producer side. The issues of high mobility in ad hoc environments are tackled by our probabilistic multicast protocol. This protocol specifically targets ad hoc environments where high node mobility and a frequently changing number of group members are present. Each node decides if it should forward a flooding message according to a probability p (0,1] which is updated according to the number of duplicates that a node has received from its neighbouring nodes. This effectively minimises the number of unnecessary duplicate messages without sacrificing reliability as we have found experimentally through simulations. The second

5 mechanism, which is called damping, aims to eliminate the number of unneeded duplicates by allowing nodes to wait for a random, small time interval before they will actually forward a message. 5.3 Real-Time Support for Mobile Ad Hoc Environments In order to support QoS guarantees in mobile ad hoc environments, a high probability of meeting deadlines has to be offered. We also believe that such infrastructure has to be adaptable and flexible to deal with the highly dynamic and unpredictable nature of the environment. Resource management plays an important role in this adaptation process in terms of both resource awareness and dynamic reallocation of resources. Crucially, fail-safe mechanisms are needed to bring the application to a safe state when timing failures are detected. Therefore, QoS management support is required for monitoring QoS violations and triggering both adaptation and fail-safe procedures when required. In addition, QoS management is required to arbitrate the allocation of network resources whereby admission control tests are performed and more resources are conceded to tasks with shorter deadlines and higher criticality.. Monitoring of QoS violations and adaptation procedures are carried out by the QoS management CF. This is achieved by the use of the timely computing base (TCB) [13] which is a framework that provides crucial time-related services. More specifically, TCB supports the detection of timing failures and enforces the timely execution of fail-safe procedures and adaptation strategies. Fail-safe procedures ensure that the system is taken to a safe state when a critical failure is detected. A coverage stability facility is provided where a high probability of meeting a deadline can be defined. In the case of going below a specified value, adaptation procedures are triggered (e.g. a redistribution of network and CPU resources). Network QoS management is achieved as follows. Firstly, every node is able to listen to traffic as the dissemination of packets is carried out by using an application level multicast protocol. Secondly, available bandwidth is fairly distributed among the nodes within a transmission area. For this purpose, a fully distributed protocol is used. Thirdly, a weighted fair scheduling policy [14] is used to allocate bandwidth within a single node to multiple service classes, each one of them associated with a particular task. A task [15] is a logical unit of computation which has an amount of resources allocated. A service class (i.e., a task) can be partitioned into sub classes (i.e., subtasks). In addition, classes and subclasses have a priority value. More critical classes have a higher priority. Different policies can be defined in case of resource contention. A detailed description of the network QoS management protocol can be found in [15]. 6. Concluding remarks We have presented a middleware architecture for the support of both pervasive and mobile ad hoc computing environments. More specifically, the sentient object model has been introduced as a central concept in the CORTEX architecture. This model provides key features for allowing both context-awareness and intelligent autonomous behaviour. In addition, the middleware is constituted by a number of component frameworks. Autonomous intelligent behaviour is addressed by both the sentient CF and the context CF. In addition, mobility and pervasiveness issues are

6 tackled by the publish/subscribe CF, the multicast CF and the service discovery CF. Lastly, the issues of time criticality in mobile ad hoc environments are addressed by both the QoS management CF and the resource management CF. Details on the implementation of the system can be found in [16]. We are currently using the vehicle test bed to evaluate the middleware architecture. Ongoing work also includes the simulation of the network QoS management protocol and the experimental evaluation of the scheduling system. Reference [1] Lee, S.-J., et al. A Performance Comparison Study of Ad Hoc Wireless Multicast Protocols. in Proceedings of IEEE INFOCOM Tel Aviv, Israel. [2] Roman, M., et al., Gaia: A Middleware Infrastructure to Enable Active Spaces. IEEE Pervasive Computing, Oct-Dec: p. pp [3] Brumitt, B., et al., EasyLiving: Technologies for Intelligent environment. Handheld and Ubiquitous Computing, September. [4] CORTEX. Preliminary Definition of the CORTEX Programming Model. CORTEX Project. IST , Deliberable D2. March [5] Veríssimo, P., et al. CORTEX: Towards Supporting Autonomous and Cooperating Sentient Entities (2002). in In Proceedings of European Wireless Florence, Italy. [6] Verissimo, P. and A. Casimiro. Event-Driven Support of Real-Time Sentient Objects. in Eighth IEEE International Workshop on Object-oriented Realtime Dependable Systems (WORDS 2003) Guadalajara, Mexico. [7] Szyperski, C., Component Software: Beyond Object-Oriented Programming. 1998, Harlow, England: Addison-Wesley. [8] Meier, R. and V. Cahill. Steam: Event-based Middleware for Wireless Ad Hoc Networks. in In Proceeding of the International Workshop on Distributed Event-Based Systems (ICDSC/DEBS'02) Vienna, Austria. [9] CORTEX. Preliminary Specification of Basic Services and Protocols. CORTEX Project. IST Deliverable D5. February [10] OMG, CORBAServices: Common Object Services Specification , Object Management Group. [11] Sun, Java Distributed Event Specification. 1998, Sun Microsystems, Inc. [12] Carzaniga, A., Rosenblum, D. and Wolf, A., Design and Evaluation of a Wide-Area Event Notification Service. ACM Transactions on Computer Systems, (3): p. pp [13] Verissimo, P. and A. Casimiro, The Timely Computing Base Model and Architecture. Transaction on Computers - Special Issue on Asynchronous Real-Time Systems, (8). [14] Demers, A., S. Keshav, and S. Schenker, Analysis and simulation of a fair queueing algorithm. Journal of Internetworking Research and Experience, 1990: p. pp [15] Duran-Limon, H.A. and G.S. Blair. A Resource and QoS Management Framework for Real-Time Event Systems in Mobile Ad Hoc Environments. in In 9th IEEE International Workshop on Object-oriented Real-time Dependable Systems (WORDS 2003F) Capri Island, Italy. [16] Sivaharan, T., et al. Cooperating Sentient Vehicles for Next Generation Automobiles. in MobiSys 2004 Workshop on Applications of Mobile Embedded Systems Boston, MA, USA.