Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

In this paper, a new dual-pocket (DP), dielectric modulated (DM) heterostructured tunnel field-effect transistor (DM-HTFET)-based biosensor has been reported. First, the efficacy of the DP-hetero-TFET (HTFET) has been analyzed by comparing it with other existing TFET structures. Next, a comprehensive assessment of sensitivity between single-pocket (SP) and DP-DM-HTFET biosensors for pocket thickness ( ${T}_{\text {pocket}}$ ), pocket length ( ${L}_{\text {pocket}}$ ), pocket doping ( ${N}_{\text {pocket}}$ ), work function of the gate metal, molar fraction of Ge, gate oxide layer, and gate oxide layer thickness ( ${T}_{\text {ox}}$ ) was done. Hence, a nonuniform arrangement of biomolecules inside the cavity simulation has been done using ATLAS device simulation software to validate the working ability of the proposed sensor. Significant improvement in the sensitivity due to threshold voltage (ON-current) i.e., 26.78% (78.5%), 60.8% (40.4%), 56% ( ${2.2} \times {10}^{{2}}$ %), and 40.6% (80.68%) has been observed for the DP-DM-HTFET over SP with the variation of ${T}_{\text {pocket}}$ , ${L}_{\text {pocket}}$ , ${N}_{\text {pocket}}$ , and ${T}_{\text {ox}}$ , respectively. DP-DM-HTFET-based current mirror circuit has also been demonstrated at the end. Sensitivity evaluation discloses that the DP-DM-HTFET can be a promising candidate for CMOS-based label-free biosensing applications.

Wireless sensor networks (WSNs) consist of an enormous number of tiny sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental information to the base station (BS) for a variety of applications. Energy efficiency is one of the primary concerns for maintaining WSN in operation. In this study, an energy efficient clustering protocol based on K‐means algorithm named EECPK‐means has been proposed for WSN where midpoint algorithm is used to improve initial centroid selection procedure. The proposed approach produces balanced clusters to ultimately balance the load of cluster heads (CHs) and prolong the network lifetime. It considers residual energy as the parameter in addition to Euclidean distance used in basic K‐means algorithm for appropriate CH selection. Multi‐hop communication from CH nodes to BS takes place depending on their distances from BS. Simulation result shows that the proposed approach outperforms LEACH‐B, balanced parallel K‐means (BPK‐means), Park's approach and Mk‐means with respect to network lifetime and energy efficiency. Simulation result also demonstrates that the proposed approach can reduce the energy consumption at most 50% compared to LEACH‐B, 14% compared to BPK‐means protocol, 10% compared to Park's approach and 6% compared to Mk‐means.

Fast interactive response in mobile cloud computing is an emerging area of interest. Execution of applications inside the remote cloud increases the delay and affects the service quality. To avoid this difficulty cloudlet is introduced. Cloudlet provides the same service to the device as cloud at low latency but at high bandwidth. But selection of a cloudlet for offloading computation at low power is a major challenge if more than one cloudlet is available nearby. In this paper we have proposed a power and latency aware optimum cloudlet selection strategy for multi-cloudlet environment with the introduction of a proxy server. Theoretical analysis show that using the proposed approach the power and the latency consumption are reduced by approximately 29-32 and 33-36 percent respectively than offloading to the remote cloud. An experimental analysis of the proposed cloudlet selection scheme is performed using cloudlets and cloud servers located at our university laboratory. Theoretical and experimental results demonstrate that using the proposed strategy power and latency aware cloudlet selection can be performed. The proposed approach is compared with the existing methods on multi-cloudlet scenario to demonstrate that the proposed approach reduces the power consumption and the system response time.