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Go to Editorial ManagerBattery life is the hard constraint for massive Internet-of-Things (IoT) at 6G scale. Wake-up radios (WuRs)—ultra-low-power auxiliary receivers that “listen” for short wake-up signals while the main transceiver sleeps—offer orders-of-magnitude energy savings, but suffer from false wake-ups, long tail latency under bursty traffic, and sensitivity/coverage limits. This paper proposes an end-to-end AI-optimized WuR stack that combines (i) a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and (ii) a reinforcement-learning (RL) scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers. The proposed method, tested using a trace-driven simulator calibrated with published WuR power/latency figures, the approach reduces node-average energy by 41–72% versus strong baselines, while meeting 99% latency targets and cutting false wake-ups by >80%.
This paper proposes Smart Adjustable Traffic light Time System (SATTS), a cloud-based IoT network model with a central management architecture by using cloud based IoT. The model innovates an algorithm to gather data from IoT devices connected to the sensors network to enable efficient traffic management in smart city appliances. SATTS scheme adjusts the green light period at intersections to alleviate traffic congestion and minimize delays and keep vehicle’s traffic conservation. The mentioned model has been proposed to modifying the timing of green lights at the four ways intersection based on the vehicles arrival rate. The results verified by comparing SATTS with Fixed time Traffic light System (FTS) and Variable Traffic light System (VTS) models, and showed that the proposed schemes can effectively decreases delay and traffic congestion. On average, there is a reduction in the rate of number of waiting cars compared to the arriving cars of 33% in cycle 2 and 50% in cycle 3 for VTS, and 52% in cycle 2 and 75% in cycle 3 for SATT compared to FTS. SATTS minimizes delay time between the four lines of the intersection.
Today, our cities are facing a host of challenges to accomplish the quality of life or their inhabitants. On the one hand, city planners and architects seek to preserve heritage, habits, and city peculiarities. On the other hand, it is necessary that the city is kept abreast of the rapid changes in Information and Communications Technology (ICT), Internet of Things (IoT), Artificial Intelligence (AI) and smart city concept. In Baghdad, it could be observed that there are several activities based on community initiatives, awareness campaigns, and initiatives which are self-funding from youth or funding from NGOs, and INGOs. How can we invest in such initiatives to achieve a smart city, emphasizing that the city is for the people, not a city of things? As we know that smart cities have six factors: smart (economy, governance, environment, people, mobility, and living)._x000D_ This paper assumes that smart communities are the seventh factor of smart cities factors which could play an essential role to apply the smartness in Baghdad. In this case, it will help to achieve making decisions and a feedback evaluation system will be subject to transparency, openness, vitality, and sustainability because it will stem from the community and ensure the sustainability in a smart city.
In Urban cities, services are supported by intelligent applications and are connected to each other through ad hoc networks. Any service can be operated using a compatible of an Internet of Things (IoT) technology. This study focuses on the transportation service and finding a non-cost solution to solve the crossroads congestion that affected people time and money. The Wireless Sensor Networks (WSNs) that are planted on the roads can help in monitoring the roads situation by collecting their data and send them through wireless communication to a traffic management center. In this work two phases of time are considered for a crowded area. Low-cost components are suggested to solve the congestion at the cross roads without the need for reconstruct the roads. IoT device such as smart phone can be wirelessly connected to the Traffic Management Center (TMC), which can analyze the incoming data from WSN and send back the calculated time to the police officer to control the green light long and overcome the standard time installed for all directions. The main idea is to solve the congestion problem in real time by extending the time long of the green traffic light for the road direction with the highest vehicle density. The suggested algorithm was operated on a dataset of 6 days and for the time phase from 7:00-10:00am.