The security attacks wireless sensor network (WSN) are increasing. These security attacks degrade the performance of WSN, e.g. energy consumption, throughput, and delay. These are biggest obstacle to make WSN greener. The aim of the paper is to model the behavior of jamming attack and analyze its effect on performance of WSN. Jamming attack jams the traffic in network by blocking the channel. The behavioral modelling and analysis of jamming attack in realistic situations (e.g. sensing in industrial application by following all network rules), gives the clear understanding of jamming attack execution. The paper also presents the possibility of jamming attack in cluster-based WSN, i.e. intelligent cluster head (CH) jamming attack. This degrades the performance of WSN around 30% more than normal reactive jamming attack. The paper finally proposes the requirements for designing the efficient solution against jamming attack.

Wireless Sensor Network (WSN) is used for the monitoring and control applications where sensor nodes gather data and send it to the sink. Most of the energy of these nodes is consumed in transmission of data packets without aggregation to sink, which may be located at single or multi hop distance. The direct transmission of data packets to the sink from nodes in the network causes increased communication costs in terms of energy, average delay and network life time. In this context the data aggregation techniques minimize the communication cost with efficient bandwidth utilization by decreasing the packet count reached at the sink. Here we propose Two Tier Cluster based Data Aggregation (TTCDA) algorithm for the randomly distributed nodes to minimize computation and communication cost. The TTCDA is energy and bandwidth efficient, since it reduces the transmission of number of packets to the sink. It is based on the additive and divisible aggregation functions at Cluster head. The functions are applied according to data packets generated by each node by considering spatial and temporal correlation. The aggregation function used in TTCDA effectively reduces the packet count reported to the sink and prevent transmission of redundant data packets by improving energy consumption. The performance of algorithm is validated using examples and simulations.

The fundamental challenge in design of Wireless Sensor Network (WSNs) is proper utilization of resources which are scare. One of the critical challenges is to maximize the bandwidth utilization in data gathering from sensor nodes and forward to sink. The main design objective of this paper is to utilize the available bandwidth efficiently in terms of reduced packet delivery ratio and throughput. Bandwidth Efficient Heterogeneity aware Cluster based Data Aggregation (BHCDA) algorithm presents the solution for the effective data gathering with in-network aggregation. It considers the network with heterogeneous nodes in terms of energy and mobile sink to aggregate the data packets. It embodies the optimal approach by Intra and inter-cluster aggregation on the randomly distributed nodes with variable data generation rate while routing data to sink. It uses the correlation of data within the packet for applying the aggregation function on data generated by nodes. BHCDA shows significant improvement in packet delivery ratio (67.66 % & 19.62 %) and throughput (37.01 % & 17.16 %) as compared with the state-of-the-art solutions, Two Tier Cluster based Data Aggregation (TTCDA) and Energy Efficient Cluster based Data Aggregation (EECDA).

Networks of the future envisage a network-of-wireless-networks that provide the end user the means to connect to the best available network at anytime and at any place. However, equally challenging for the operators is to provide these services at low cost in an era where spectral resources are a premium. This paper investigates cooperation between networks based radio access technology (RAT) selection algorithm that uses suitability to optimize the choice between WiFi and high speed downlink packet access (HSDPA). It has been shown that this approach has the potential to provide gain by allocating a user terminal to the most preferred network based on traffic type and network load. Optimal load threshold values that maximise the total QoS throughput for the given interworking scenario are 0.6 and 0.53 for HSDPA and WiFi, respectively. This corresponds to a CRRM gain on throughput of 80% with 60 users.