The recent technological advances in micro electromechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of ubiquitously retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. However, satisfying the stringent quality of service (QoS) requirements of multimedia transmission in a resourceconstrained sensor network environment places new challenges to routing. As an outcome, optimal energy and applicationspecific QoS aware routing for WMSNs has gained considerable research attention recently. In this paper, current state-of-the-art in energy-efficient routing techniques for WMSNs is surveyed together with the highlights of the performance issues of each strategy. We outline the design challenges of routing protocols for WMSNs followed by the limitations of current techniques designed for non-multimedia data transmission. Further, a classification of recent routing protocols for WMSNs and a discussion of possible future research trends are presented. Index Terms-Micro Electro-Mechanical systems (MEMS), Wireless Multimedia Sensor Networks (WMSNs), Quality-ofService (QoS).

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Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data centers. The proposed framework: i) predicts the number of virtual machine (VM) requests, to be arriving at cloud data centers in the near future, along with the amount of CPU and memory resources associated with each of these requests, ii) provides accurate estimations of the number of physical machines (PMs) that cloud data centers need in order to serve their clients, and iii) reduces energy consumption of cloud data centers by putting to sleep unneeded PMs. Our framework is evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. These evaluations show that our proposed energy-aware resource provisioning framework makes substantial energy savings.