Budget constrained relay node placement problem for maximal “connectedness”

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

“…In [40], the connectivity analysis models of WSNs under unicast and broadcast were provided. The study in [44] considered the deployment of sensor networks under the most general constraints. The study in [41] proposed three clustering algorithms to analyze the leading indicators of network connectivity, including availability indicators, time indicators, and reliability indicators.…”

“…[30] 2-D Heuristic Algorithm [31] Network uniformity, deployment time 2-D Heuristic Algorithm [32] Energy consumption 2-D Mathematical optimization [33] Time to calculate the fitness function 2-D Heuristic Algorithm [34] Power consumption of SNs 2-D Heuristic Algorithm [35] 2-D Graph theory, Heuristic Algorithm [36] Coverage duration 2-D Heuristic Algorithm [37] Target detection 2-D Mathematical optimization [38] Target detection 2-D Mathematical optimization [39] 2-D Heuristic Algorithm [40] The scheme of broadcast,unicast 3-D Mathematical optimization [41] Indicator of availability,time,reliability 2-D Mathematical optimization [42] 2-D Path coloring, Mathematical optimization [43] 2-D Graph theory [44] Fixed budget 2-D Heuristic Algorithm [45] Residual energy of the SNs 2-D Heuristic Algorithm [46] Sent Bytes, movement cost of SNs 2-D Mathematical optimization [47] Power assignment 2-D Heuristic Algorithm [48] The number of relay nodes 2-D Graph theory [49] The number of sensors to activate 2-D Mathematical optimization [50] 2-D Heuristic Algorithm [51] 2-D Heuristic Algorithm [52] The number of SNs 2-D Heuristic Algorithm [53] 2-D Mathematical optimization [54] 2-D Heuristic Algorithm [55] 2-D Mathematical optimization [56] 2-D Heuristic Algorithm [57] 2-D Mathematical optimization [58] The number of SNs 2-D Graph theory, Heuristic Algorithm [59] 2-D Heuristic Algorithm [60] The wakeup scheduling scheme of SNs 3-D Heuristic Algorithm [61] 3-D Heuristic Algorithm Ours…”

A Continuum Model for Route Optimization in Large-Scale Inhomogeneous Multi-Hop Wireless Networks

“…We denote the Euclidean distance between a point at (r, φ) and (r m , φ m ) by d m where d 2 m = r 2 + r 2 m − 2rr m cos(φ − φ m ). If we extend the analysis detailed in section IV to multiple independently faded interferers then (20) becomes…”

“…In the multihop context, these problems are often referred to as the relay node placement problem (RNPP) where the objective relates to the deterministic placement of the minimum number of relay nodes that meet the design objectives. The RNPP has been proven to be NP-hard [17]- [19] and even with a relatively low number of relays, the problem is non trivial and can also be NP-complete [20], [21]. This optimization challenge has been extensively researched and the common theme is the non-trivial nature of the optimization problem that necessitates heuristic or meta-heuristic [22], [23] techniques to deliver a sub-optimal solution.…”

Relay node placement in wireless sensor network for manufacturing industry

“…Unlike [7], [13], which consider optimizing the wireless network, we focused on a sensor node environment with the appropriate placement of relay nodes. We investigate the connectivity of the sensor by leveraging the prim's algorithm presented in [22] and the approximation k-minimum spanning tree (k-MST) algorithm [25]. Our work develops the system model based on a wireless network cluster environment, subject to budget constraints conditions for the manufacturing industries.…”