Dividing a network into clusters is a well-established strategy to address scalability challenges in data collection. With the advent of multi-access edge computing, unmanned aerial vehicles (UAVs) are increasingly being deployed to collect data from these clusters. For efficient and scalable UAVassisted data collection, the cluster formation, beyond being energy efficient, must also satisfy several critical properties, such as maintaining uniformity in cluster shape and size, ensuring appropriate placement of cluster heads, and forming compact, multi-hop structures. When nodes are mobile, clustering also needs to be fast enough to remain consistent despite topology dynamics. Existing approaches struggle to balance these diverse requirements effectively. We propose ReClub, a novel Concurrent-Transmission (CT)-based clustering framework that enables ultra-fast, energy-efficient, and topology-aware cluster formation for UAV-assisted data collection. Extensive evaluations demonstrate that ReClub is up to 92% faster than state-of-theart approaches, while sustaining reliable data collection under mobility. Moreover, ReClub-based data aggregation supports up to 7 × higher information refresh rates, making it highly suitable for next-generation mobile and large-scale edge-assisted IoT applications where 'age of information' is critical.

The integration ofDigital Twin(DT) into Metaverse plays a significant role in providing virtual replicas of the real world.Internet of Things(IoT) enables thePhysical Entities(PE) of the real-world to get appropriately mapped with their respective DTs. In recent works,Multi-Access Edge Computing(MEC)-based architecture shows faster mapping solution through UAV-based real-time data-collection from the PEs. Energy constraint in both the PEs and the UAVs is an important aspect in the whole process. Recent advancements in energy harvesting technologies directly benefit the PEs, the energy constraint in UAVs is still a stringent factor. Unfortunately existing works fail to address both the speed of data-collection as well as energy consumption of the UAVs together. In this work, we propose aConcurrent-Transmission(CT)-assisted mechanism, FETCH which addresses both of these factors together. In contrast to existing approaches, FETCH exploits CT to realize efficient coordination among the PEs so that a significant part of the data-collection can be done in parallel through the PE network. This helps the UAVs to significantly reduce their movements enhancing the utilization of their battery capacity. Evaluation shows that FETCH enables up to 88% faster and 89% energy efficient data-collection compared to the existing best known approaches. Moreover, it provides seamless support for change in topology of PEs. We also show that FETCH accomplishes a significantly higher rate of PE-DT mapping realizing superior quality of service.

A modern IoT-system, to accomplish various sophisticated goals, requires support for a variety of information management services, such as data-collection, gathering, routing and several others. There has been plenty of research on devising dedicated strategies to serve these goals. In recent works, Synchronous-Transmission (ST)-based mechanisms have shown promising solutions. However, in the context of low-power and highly resource-constrained IoT-edge, employing explicit dedicated solutions for multiple frequently encountered issues makes the system sufficiently complex as well as quite inefficient in terms of the memory and energy constraints of the IoT-devices. The availability of a topological structure in the system brings forth a way to accomplish versatile information management goals uniformly under a single hood. It also binds the nodes in a distributed fashion, with a grip over the entire system. However, the existing strategies to accomplish such hierarchical organization in an IoT-system is too slow to serve dynamic requirements. In this work, we propose an ST-based strategy, TurTle to accomplish the goal in real-time. Furthermore, we also design simple applications of TurTle to show its effectiveness. Extensive simulation-based studies demonstrate that TurTle builds the hierarchical structure upto 55 % faster than the existing bestknown approach.

In advanced applications of Internet of Things (IoT), sharing of multimedia data is one of the common requirements which is not explicitly addressed in the existing works. The constraints of the low-power IoT-devices in their processing capability, memory, and energy, make efficient coordination based on the shared data quite challenging. In this paper, we study one such situation in the context of an IoT-assisted lightweight Intelligent-Transportation System (ITS). A many-tomany data-sharing strategy called ChainBus is proposed for fast sharing of bulk-data among the Road-Side Units (RSU) in ITS. Next, an efficient inter-RSU coordination mechanism is designed to dynamically infer the road-connectivity structure among the RSUs without the help of cloud. We show that our strategy can achieve its goal upto 54.3% faster compared to the solutions obtained by the existing best-known strategies.

Detection of vehicles and their classification is a significant component of wide-area monitoring and surveillance, as well as intelligent- transportation . Existing solutions tend to employ heavy-weight infrastructure and costly equipment, as well as largely depend on constant support from the cloud through round-the-clock internet connectivity and uninterrupted power supply. Moreover, existing works mainly concentrate on localized measurement and do not discuss their efficient integration to address the problem over a wide area. For practical use in an outdoor environment, apart from being technically sound and accurate, a solution also needs to be cost-effective , lightweight , easy to install , flexible , low overhead , and easily maintainable , as well as self-sufficient as much as possible. However, fulfilling all these goals together is a challenging task. In this work, we propose an IoT-assisted strategy, LiVeR , to accomplish it. For self-sufficient on-the-fly classification in resource-constrained low-power IoT devices, LiVeR minimizes not only the computational requirements but also the energy consumption, which enables sustained operation in a hostile outdoor environment for a considerably long time solely based on battery power. Through extensive studies based on outdoor measurement and trace-based simulation on empirical data, we demonstrate that LiVeR classifies vehicles of small, medium, and large size with an accuracy of 91.3% up to 98.8%, 92.3% up to 98.5%, and 93.8% up to 98.8%, respectively, for single-lane traffic. We also demonstrate that LiVeR spends only about one-third of the number of RF packets to achieve vehicle detection and classification compared to the state-of-the-art RF-based solution, considerably extending the lifetime of the system.

A significant part of an IoT-based smart-system is comprised of a large number of low-cost devices that interact with one another based on low-power wireless communication. Energy-efficient and fast sharing of data plays a crucial role in such massive distributed systems. The topology of the underlying network highly influences the efficiency of a data-sharing process. However, since topological information becomes available only after the deployment of a system, it is hard to design a strategy paying proper attention to this important issue. In this work, we explore how many-to-many data-sharing, which is the most complex and highly important pattern of sharing data, can benefit from the availability of network topology information. We also deal with how the necessary topology information can be obtained in run-time. In particular, we design a Synchronous-Transmission (ST)-based many-to-many data-sharing strategy that can dynamically modulate itself in real-time as per the topology of the underlying network. Through extensive simulation as well as experiments over publicly available IoT-testbeds we demonstrate that the proposed strategy can accomplish all-to-all data-sharing in IoT up to 42.5% faster and consume 43.3% less radio-on time compared to the existing state-of-the-art strategies.

In the near future, a multi-drone conveyance system will be an invaluable asset for meeting a variety of local and lightweight transportation needs. Recent research has focused extensively on the design and development of efficient multi-drone conveyance systems. However, the majority of these works rely on centralized cloud-based support, which is significantly susceptible to various single-point-of-failure-related issues. In this paper, a distributed IoT-assisted framework for multi-drone transportation is proposed. However, a successful distributed implementation of the system requires extensive coordination between its various components. Particularly, the drones need to tightly collaborate with the stations where they can temporarily halt for charging, referred to as Charging and Resting Stations (CRS). In this paper, we propose utilizing recent advancements in Synchronous-Transmission (ST)-based communication strategies to realize a fast and energy-efficient communication backbone for multi-drone transportation systems. Through the implementation of a simple dynamic path planning strategy, we demonstrate that the proposed framework outperforms the implementation of the multi-drone system using conventional communication strategies in terms of delivery time and reliability. In addition, we demonstrate that the strategy not only handles random conveyance assignments efficiently but also fairly distributes the load across the CRSs.

Advancements in Synchronous-Transmission (ST)-based strategies have brought drastic improvement in data-sharing in IoT. Many-to-many/all-to-all data-sharing is one of the most critical and complex patterns of data-sharing. To systematically address the complexity, ST-based strategies adopt a very well-defined setting which although apparently solves the problem, produces solutions that are relatively rigid and tightly coupled with the system. These solutions also focus on achieving global-outreach of the data from the source nodes. In contrast, when a system spreads over a large geographic area and contains proportionately many nodes, instead of global-outreach, it is more essential to have a facility for fast, and efficient local interaction/data-sharing among the neighboring nodes uniformly throughout the system. Unfortunately, the existing ST-based strategies fail to satisfy such requirements efficiently due to a lack of flexibility. In this work, we show how to incorporate controlled flexibility in ST-based many-to-many data-sharing. We design and implement a protocol LiteCast to accomplish efficient local interaction/coverage in the system irrespective of its span. Through extensive studies in publicly available IoT-testbeds we show that LiteCast consumes upto 67.5% lesser time in accomplishing local-coverage compared to the existing best-known many-to-many data-sharing strategies,

Advancements in the domain of Synchronous- Transmission (ST) have brought a drastic improvement in the all-to-all data-sharing strategies in low-power wireless distributed systems. However, in the current design of the ST-based protocols, the inability of overhearing from the neighbors, and the lack of simultaneous system-wide commencement of the target process act as major bottlenecks in the faster percolation of data. The physical layer phenomena Capture-Effect (CE) is considered to be one of the fundamental strengths of ST. However, since the scope of CE largely depends on network topology, an ST-based protocol also mostly fail to exploit the full benefit of CE. Existing strategies try to exploit the topology information implicitly through on-the-fly exploration which, however, results in only partial advantage. In this work, we first introduce a few fundamental design ingredients to accelerate the percolation of data. Next, we propose a TDMA-based self-adaptive all-to-all data-sharing strategy SyncCast which explicitly learns the topology of the underlying network in real-time, and then on-the-fly optimizes the transmission plans to maximize the advantage from CE. Through extensive experiments in IoT-testbeds we demonstrate that despite being simple, SyncCast is upto 65 % faster and consumes 74% less Radio-on time compared to the existing state-of-the-art solution for all-to-all data-sharing.

Divide, Conquer and Merge (DCM) is a well-known algorithmic paradigm. It has been used to solve both core problems as well as to address the scalability issue in other algorithms. It is widely used in networks or distributed computation to solve the scalability problem arising due to highly dense and wide structures. However, from the implementation point of view, all these application of DCM were designed considering the traditional Asynchronous-Transmission (AT) based communication technologies. In this work, we study the application of DCM in the context of Synchronous-Transmission (ST) which bears fundamentally different dynamics compared to the AT. Specifically, we study the application of DCM over an existing state-of-the-art ST-based strategy MiniCast and demonstrate the performance gain in calculation of aggregation. Our work, shows that applying DCM on MiniCast makes MiniCast based aggregation mechanism upto six times faster and make it consume upto five times lesser energy compared to the case when DCM is not applied.

An efficient Home Area Network (HAN) acts as a base of an Advanced Metering Infrastructure (AMI). A HAN not only facilitates AMI with efficient real-time monitoring of the electricity consumption but also manages the load profile of the whole system. However, the existing works on implementing HAN are mostly centralized and suffer from well-known problems. In this work, we propose an IoT-based efficient decentralized strategy using synchronous transmission to practically realize HAN. An inter-device coordination strategy is proposed to minimize the peak load as well as reduce the sudden changes in the overall system without compromising the user’s requirements. Through experiments over IoT-testbeds, we demonstrate that the proposed strategy can reduce the peak load upto 50% and reduce the load variations upto 58% for even a high and random rate of requests for execution of power-hungry house appliances.

Intra-group spreading is one of the common and frequent needs in many decentralized communication protocols. Such spreading is useful for quick and local dissemination of information among different clusters in large-scale decentralized systems like the Internet-of-Things (IoT). Complex decentralized protocols can carefully exploit such localized dissemination as a base unit for their efficient implementation. However, due to the inherent broadcast nature of wireless communication, efficient and simultaneous execution of multiple intra-group disseminations is difficult. In this work, we propose a novel and simple way to completely hide a wireless transmission without changing any channel or frequency. Next, we use this for supporting simultaneous intra-group disseminations. Rigorous evaluation of the proposed strategy over testbeds show significant improvement of upto 60% in reliability with similar average latency and radio-on time in comparison to the baseline where no additional mechanism is adopted for separation of intra-group communications.

Simultaneous intra-group communication is one of the possible ways to handle the scaling issues in large decentralized systems. FDMA is an elegant strategy to carry out intra-group activities in parallel. However, due to increasing congestion in the licence free ISM bands, especially in the urban area, its hard to get enough number of good standard channels for multi-group setting in decentralized systems like IoT/WSN. In this work, we leverage concurrent transmission for efficient simultaneous intra-group one-to-all dissemination. Concurrent transmissions based strategies schedule the packet-transmissions in a special way so that the overlapping packets, instead of colliding with each other, result in constructive-interference or capture-effect. We propose a simple strategy to exploit this feature under concurrent transmission to achieve fruitful simultaneous intra-group communication in overlapping channels. Through extensive evaluation in testbeds for WSN/IoT under 2.4 GHz ISM band we show that, the proposed strategy can efficiently support simultaneous intra-group one-to-all dissemination even with allocation of channels having Center Frequency Distance (CFD) 1 MHz in most of the common group divisions. Experiments with a wide variety of group divisions also reveal that under concurrent transmission a CFD of 3 MHz and above can tolerate strong inter-group interference when the groups are quite mixed with each other which can be quite common specially when the nodes are mobile.