Large scale wireless sensor networks (WSNs) can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA) is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.

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In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms.

As typical wireless sensor networks (WSNs) have resource limitations, predistribution of secret keys is possibly the most practical approach for secure network communications. In this paper, we propose a key management scheme based on random key predistribution for heterogeneous wireless sensor networks (HSNs). As large-scale homogeneous networks suffer from high costs of communication, computation, and storage requirements, the HSNs are preferred because they provide better performance and security solutions for scalable applications in dynamic environments. We consider hierarchical HSN consisting of a small number high-end sensors and a large number of low-end sensors. To address storage overhead problem in the constraint sensor nodes, we incorporate a key generation process, where instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and a publicly known seed value, a keyed-hash function generates a key chain; these key chains collectively make a key pool. As dynamic network topology is native to WSNs, the proposed scheme allows Scalable and efficient key management for heterogeneous 45 dynamic addition and removal of nodes. This paper also reports the implementation and the performance of the proposed scheme on Crossbow's MicaZ motes running TinyOS. The results indicate that the proposed scheme can be applied efficiently in resource-constrained sensor networks. We evaluate the computation and storage costs of two keyed-hash algorithms for key chain generation, HMAC-SHA1 and HMAC-MD5.

Key distribution refers to the problem of establishing shared secrets on sensor nodes such that secret symmetric keys for communication privacy, integrity and authenticity can be generated. In a wireless sensor network, pre-distribution of secret keys is possibly the most practical approach to protect network communications but it is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor networks. In this paper, we propose a key distribution scheme based on random key pre-distribution for heterogeneous sensor network (HSN) to achieve better performance and security as compared to homogeneous network which suffer from high communication overhead, computation overhead, and/or high storage requirements. In a key generation process, instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and publicly known seed value, a one-way hash function generates a key chain, and these key chains collectively make a key pool. Each sensor node is assigned a small number of randomly selected generation keys. The proposed scheme reduces the storage requirements while maintaining the same security strength.

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Virtual multiple input multiple output (MIMO) techniques are used for energy efficient communication in wireless sensor networks. In this paper, we propose energy efficient routing based on virtual MIMO. We investigate virtual MIMO for both fixed and variable rates. We use a cluster based virtual MIMO cognitive model with the aim of changing operational parameters (constellation size) to provide energy efficient communication. We determine the routing path based on the virtual MIMO communication cost to delay the first node death. For larger distances, the simulation results show that virtual MIMO (2×2) based routing is more energy efficient than SISO (single input single output) and other MIMO variations.