Gateways are emerging as a key element of bringing legacy and next generation devices to the Internet of Things (IoT). They integrate protocols for networking, help manage storage and edge analytics on the data, and facilitate data flow securely between edge devices and the cloud. Current IoT gateways solve the communication gap between field control/sensor nodes and customer cloud, enabling field data to be harnessed for manufacturing process optimization, remote management, and preventive maintenance. However, these gateways do not support fully-automatic configuration of newly added IoT devices. In this paper, we proposed a self-configurable gateway featuring real time detection and configuration of smart things over the wireless networks. This novel gateway's main features are: dynamic discovery of home IoT device(s), automatic updates of hardware changes, connection management of smart things connected over AllJoyn. We use the `option' field for automatic configuration of IoT devices rather than modify standard format of CoAP protocol. Proposed gateway functionality has been validated over the large-scale IoT testbed.

With the growing interest in wireless sensor networks (WSNs), minimizing network delay and maximizing sensor (node) lifetime are important challenges. Since the sensor battery is one of the most precious resources in a WSN, efficient utilization of the energy to prolong the network lifetime has been the focus of much of the research on WSNs. For that reason, many previous research efforts have tried to achieve tradeoffs in terms of network delay and energy cost for such data aggregation tasks. Recently, duty-cycling technique, i.e., periodically switching ON and OFF communication and sensing capabilities, has been considered to significantly reduce the active time of sensor nodes and thus extend network lifetime. However, this technique causes challenges for data aggregation. In this paper, we present a distributed approach, named distributed delay efficient data aggregation scheduling (DEDAS-D) to solve the aggregation-scheduling problem in duty-cycled WSNs. The analysis indicates that our solution is a better approach to solve this problem. We conduct extensive simulations to corroborate our analysis and show that DEDAS-D outperforms other distributed schemes and achieves an asymptotic performance compared with centralized scheme in terms of data aggregation delay.

Emergency alert systems serve as a critical link in the chain of crisis communication, and they are essential to minimize loss during emergencies. Acts of terrorism and violence, chemical spills, amber alerts, nuclear facility problems, weather-related emergencies, flu pandemics, and other emergencies all require those responsible such as government officials, building managers, and university administrators to be able to quickly and reliably distribute emergency information to the public. This paper presents our design of a deep-learning-based emergency warning system. The proposed system is considered suitable for application in existing infrastructure such as closed-circuit television and other monitoring devices. The experimental results show that in most cases, our system immediately detects emergencies such as car accidents and natural disasters

Wireless sensor networks (WSNs) consist of multiple sensor nodes, which communicate with each other under the constrained energy resources. Retransmissions caused by collision and interference during the communication among sensor nodes increase overall network delay. Since the network delay increases as the node's waiting time increases, the network performance is reduced. Thus, the link scheduling scheme is needed to communicate without collision and interference. In the distributed WSNs environment, a sensor node has limited information about its neighboring nodes. Therefore, a comprehensive link scheduling scheme is required for distributed WSNs. Many schemes in the literature prevent collision and interference through time division multiple access (TDMA) protocol. However, considering the collision and interference in TDMA-based schedule increases the delay time and decreases the communication efficiency. This paper proposes the distributed degree-based link scheduling (DDLS) scheme, based on the TDMA. The DDLS scheme achieves the link scheduling more efficiently than the existing schemes and has the low delay and the duty cycle in the distributed environment. Communication between sensor nodes in the proposed DDLS schemes is based on collision avoidance maximal independent link set, which enables to assign collision-free timeslots to sensor nodes, and meanwhile decreases the number of timeslots needed and has low delay time and the duty cycle. Simulation results show that the proposed DDLS scheme reduces the scheduling length by average 81%, the transmission delay by 82%, and duty cycle by over 85% in comparison with distributed collision-free low-latency scheduling scheme.