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As an important application domain of wireless sensor networks (WSN), wireless home sensor network (WHSN) can be built as a traditional WSN. However, when we consider its own character that plug-in sensors are fixed with AC power supply while mobile sensors are battery powered, traditional WSN techniques do not match well. In this paper, we propose a smart wireless home sensor network architecture with improved routing protocols. It is a hierarchical architecture in which AC-powered sensors act as backbone nodes for data retransmission, while battery-powered sensors act as leaves that only transmit data relevant to themselves. Each sensor is assigned with a prime number as its location identifier. For our routing algorithm, the LID is used as routing address and is decomposed to a sequence of prime numbers that indicates the route towards its destination. In addition, we improve existing routing algorithms such as SPIN, LEACH, and DD to incorporate traditional WSN routing algorithms into our smart WHSN architecture, and comparable efficiencies are made between them. Moreover, we propose a network path recovery algorithm for failures that are caused by node mobility or backbone node failures. All our algorithms have been provided with faithful simulations to verify the feasibility and efficiency. [ABSTRACT FROM AUTHOR]

Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory. [ABSTRACT FROM AUTHOR]