Demand response (DR) is emerging as the workhorse of achieving energy efficiency and reducing our carbon footprint, which persists as a major challenge amongst all the different energy-chain players, i.e., the utility providers, policy makers, consumers, and the technology sector. For instance, the Internet-of-Things (IoT) paradigm and network-enabled appliances/devices have escalated the expectations of what technology could do for the acceptance of DR programs. In this work, we design, deploy on a scalable pilot testbed, and evaluate a collaboration-based approach to the demand-side management of a community of electricity consumers that jointly targets green consumption. The design of the framework architecture is centralized via the so-called aggregator, which optimizes the demand scheduled by consumers along with their time frame preferences towards the maximization of the consumption of renewables. On the pilot, we opt for lightweight, yet efficient platforms such as Raspberry Pi boards, and evaluate them over a series of network protocols, i.e., MQTT-TLS and CoAP-DTLS, paying special attention to the security and privacy of the communications over Z-Wave, ZigBee, and WiFi. The experiments conducted are configured using two active Living Labs datasets from which we extract three community scenarios that vary according to the flexibility or rigidity of the appliances’ operation time frame demand. During the performance evaluation, processing and communication overheads lie within feasible ranges, i.e., the aggregator requires less than 2 s to schedule a small consumer community with four appliances, whereas the latency of its link to households’ controllers adds less than 100 ms. In addition, we demonstrate that our implementations running over WiFi links and UDP sockets on Raspberry Pi 4 boards are fast, though insecure. By contrast, secure CoAP (with DTLS) offers data encryption, automatic key management, and integrity protection, as well as authentication with acceptable overheads.
Data Encryption and Fragmentation in Autonomous Vehicles Using Raspberry Pi 3
