Abstract: The rapid advancement of hardware technology has enabled the development of small, powerful, and inexpensive sensor nodes, which are capable of sensing, performing computation and wireless communication. This modernizes the deployment of wireless sensor network for monitoring some vicinity and collecting required information. However, sensor nodes have limited energy constraint which is a major challenge for such visionto become reality. We consider energy constrained wireless sensor network deployed over a region. The main job of such a network is to gather information from node and transmit it to sink node for further processing. So the aim of any data forwarding protocol is to conserve energy to maximize the network lifespan. Sensor nodes are capable of performing in-network aggregation of data coming from more than one source.In this paper we focused on energy consumption issue and aim to develop an energy efficient data aggregation protocol. To deliver energy efficiency we have co

• Publication Date: 13-Jul-2013
• DOI: 10.15224/978-981-07-7021-1-14
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Abstract: Today enterprises are facing increasing problems in adapting to sudden changes in the market requirements as also to cater to multiple geographies and to shorter product lifecycle times. Various supply chain problems like parts paucity, excessive finished good inventories, unused plant capacity, excessive warehousing costs, and inefficient transportation of supplies and finished goods are causing huge losses to companies. E-supply chain management using software agents has the benefit of solving the tasks by various participants in the supply chain network through their local intelligence and problem-solving paradigms. Using agent-based technologies, various stakeholders, suppliers, manufacturers and transporters can negotiate through dialogue and interaction mechanisms and distribute various activities in a decentralized manner more efficiently. Applications of software agents in various activities of the supply chain like production planning, production monitoring, workflow modeling,

• Publication Date: 13-Jul-2013
• DOI: 10.15224/978-981-07-7021-1-15
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• Downloads: 0