Abstract—This paper describes a sensor network architecture, called BiSNET, which addresses several key issues in wireless sensor networks such as autonomy, adaptability, self-healing and simplicity. Based on the observation that various biological systems have developed mechanisms necessary to overcome these issues, BiSNET follows certain biological principles such as decentralization, food gathering/storage and natural selection to design sensor networks. This paper describes and evaluates the biologically-inspired mechanisms in BiSNET. Simulation results show that BiSNET allows sensor nodes to autonomously adapt their duty cycles for power efficiency and responsiveness of data transmission, to collectively self-heal (i.e., detect and eliminate) false positives in their sensor readings, and to be lightweight. 1.

This article describes the new concept of service oriented software architecture for mobile sensor networks. By the use of this architecture, a flexible, scalable programming of applications based on an adaptive middleware is possible. The middleware supports mechanisms for cooperative data mining, self-organization, networking, and energy optimization to build higher-level service structures. The development of applications speeds up and is simplified by the use of optional administration components. 1

Autonomous wireless devices such as sensor nodes and stereo cameras, due to their low cost of operation coupled with the potential for remote deploy-ment, have found a plethora of applications ranging from monitoring air, soil and water to seismic detection and military surveillance. Typically, such a net-work spans a region of interest with the individual nodes cooperating to detect events and disseminate information. Given a deployment of sensors and tar-gets over a region, a sensor pairing is desired for each target that optimizes the coverage under certain constraints. This problem can be modeled as an integer programming problem and solved using branch-and-cut. For larger problems, it is necessary to limit the number of variables, and a GRASP routine was de-veloped for this purpose. Valid cutting planes are developed and computational results presented.

Abstract-In recent years, the proliferation of mobile computing devices has driven a revolutionary change in the computing world. The nature of ubiquitous devices makes wireless networks the easiest solution for their interconnection. This has led to the rapid growth of several wireless systems like wireless ad hoc networks, wireless sensor networks etc. In this paper we have proposed a framework for rural development by providing various e-services to the rural areas with the help of wireless ad hoc and sensor networks. We have discussed how timely and accurate information could be collected from the rural areas using wireless technologies. In addition to this, we have also mentioned the technical and operational challenges that could hinder the implementation of such a framework in the rural areas in the developing countries.

Software development for wireless sensor networks requires novel programming paradigms and technologies. This article describes the concept of a new service oriented software architecture for mobile sensor networks. With this architecture, a flexible, scalable programming of applications based on an adaptive middleware is possible. The middleware supports mechanisms for cooperative data mining, self-organization, networking, and energy optimization to build higher-level service structures. The purpose of our research activities is the development of a framework, which radically simplifies the development of software for sensor network applications.

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Wireless sensor networks (WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a biologically-inspired architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: data collection, event detection and hybrid applications. Each application is implemented as a decentralized group of software agents, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

This paper describes BiSNET (Biologically-inspired architecture for Sensor NETworks), which addresses several key issues in multi-modal wireless sensor networks such as autonomy, adaptability, self-healing and simplicity. Based on the observation that various biological systems have developed mechanisms to overcome these issues, BiSNET implemenets certain biological mechanisms such as energy exchange, pheromone emission, replication, and migration to design sensor network applications. This paper presents the biologically-inspired mechanisms in BiSNET, and evaluates their impacts on the issues described above. Simulation results show that BiSNET allows sensor nodes to autonomously adapt their duty cycle intervals for power efficiency and responsiveness of data transmission, to adaptively aggregate data from different types of sensor nodes, to collectively self-heal (i.e., detect and eliminate) false positive sensor data, and to be lightweight.