Data collection model for energy-efficient wireless sensor networks Nidhi Gautam 1 & Sanjeev Sofat 2 & Renu Vig 3 Received: 27 November 2014 /Accepted: 8 July 2015 # Institut Mines-Télécom and Springer-Verlag France 2015 Abstract To deal with real life scenarios for wireless sensor networks with uneven contours, connectivity issues, and dropping packets, heterogeneous sensors became the vital fac- tor to enhance its capability in terms of energy efficiency and end-to-end packet delay. In recent times, end-to-end packet delay has a significant role in wireless sensor networks along with energy efficiency and network lifetime. In the present situation, the information delayed is information lost, and hence, end-to-end packet delay is playing an important role in wireless sensor networks. To address the issue of end-to- end packet delay in wireless sensor network, a mobile cluster- head data collection model for heterogeneous wireless sensor networks has been evaluated. In this paper, the mobile cluster- head data collection model has been evaluated for two differ- ent scenarios. This paper also illustrates the velocity of the cluster-head node with which it should move to reduce the end-to-end packet delay. The mobile cluster-head data collec- tion mobility model has been evaluated for end-to-end packet delay on the basis of data send rate, network size, sensor node density, and cluster-head node density. For verification and validation, extensive simulations have been conducted which validates that the efficient mobility pattern of the mobile cluster-head nodes can lower end-to-end packet delay of wire- less sensor networks. Keywords Mobile cluster-head . Mobile data collector . Average end-to-end packet delay . Data-delivery ratio . Energy consumed Abbreviations VAS Voronoi ant systems VCP Voronoi control packet MTWSW Modified two-way sliding window MCHDC Mobile cluster-head data collection WSN Wireless sensor network HP Head point MAC Media access control BSN Base station node (sink node) 1 Introduction The transformation of network technology from massive instru- mentation to miniaturized devices has supplemented the growth of wireless networks as a ubiquitous medium for moving infor- mation across various domains. The technology has proved its existence for monitoring and interacting with physical parame- ters like humidity, temperature, pollution, etc. Wireless sensor nodes coordinate with each other and work for a common goal of data sensing, information gathering, and information dissem- ination either single-hop or multiple-hops without human inter- vention [1]. This quality entices the researchers to make it more suitable for defense as well as for other applications. To enhance the performance of data acquisition, many techniques have been proposed in the past literature, which comprises clustering [2], data aggregation [3], data fusion [4, 5], heterogeneous networks architecture [6], and use of mobile devices [7]. Clustering helps to form small-sized clusters that * Nidhi Gautam nidhig121@gmail.com 1 U.I.A.M.S., Panjab University, Chandigarh, India 10014 2 PEC University of Technology, Chandigarh, India 10012 3 U.I.E.T., Panjab University, Chandigarh, India 10014 Ann. Telecommun. DOI 10.1007/s12243-015-0471-x