Internet of Things in Agricultural Innovation and Security

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

Smart transmission power control for dependable wireless sensor networks

Smart Transmission Power Control for Dependable Wireless Sensor Network

In-node cognitive power control in Wireless Sensor Networks

\u3cp\u3eReliability, interoperability and efficiency are fundamental in Wireless Sensor Network deployment. Herein we look at how transmission power control may be used to reduce interference, which is particularly problematic in high-density conditions. We adopt a distributed approach where every node has the ability to learn which transmission power is most appropriate, given the network conditions and quality of service targets. The status of the network is represented by the combination of three parameters: number of retransmissions, clear channel assessment attempts and the quantized average latency. The target is to maintain packet loss at the lowest possible level, whilst striving for minimum transmission power. The learning phase is managed by an ϵ-greedy strategy, which directs the physical layer of each node to choose between either a random action (exploration) or the best action (exploitation). We demonstrate as our learning sensors automatically discover the best trade off between power and quality.\u3c/p\u3

Improving energy efficiency in TDMA passive optical networks from theory to practice

Passive optical networks are currently the major contributor to fixed optical networks energy consumption. Within PON, almost 65% of their energy consumption is due to the customer premises equipments (i.e., the ONUs). Standardisation authorities, industries and researchers are proposing several methods for decreasing ONU energy consumption. This paper describes a method for maximizing energy savings while providing services with delay guarantees. The method exploits cyclic sleep with service-based variable sleep periods. Simulation results prove the method effectiveness with Poisson traffic. The ongoing work in implementing the method in a testbed is also presented

Energy Efficient PONs with Service Delay Guarantees

Passive Optical access Networks (PONs) are currently the major contributor to the energy consumption budget of fixed optical networks. In PON, the largest part of the energy consumption is due to the equipments at the customer premises. This paper proposes a method for maximizing energy savings while providing services with delay guarantees (i.e., frame delivery time and frame delay variation).The method combines service-based variable sleep period and a queueing theory model to compute the optimal sleep time. Simulation results prove the effectiveness of the method for a Poisson frame arrival process