Robust Wireless Network Jamming Problems

Revisiting wireless network jamming by SIR-based considerations and multiband robust optimization

“…In the previous section, we have considered a deterministic version of the NJP, namely we have assumed to know exactly the value of all data involved in the problem. However, in practice this assumption is likely to be not true, as also discussed in [13,14]: assuming to possess a complete knowledge about the unfriendly network is unrealistic, especially in defence and security applications, where it may be very difficult or even dangerous to gather accurate information. Instead it is rational to assume that we can just rely on estimates of the position and the radio-electrical configuration of the TRXs.…”

“…where JAM t = δ j∈J m∈M a tj · P m JAM ·ȳ jm is the total jamming power that t receives for jamming solution (z,ȳ). Constraints (13)- (14) enforce the structure of the uncertainty set RHS-MB: the first family imposes the lower and upper bounds on the number of RHS values ∆SIR t that may deviate in each band k ∈ K, whereas the second family imposes that each value ∆SIR t deviates in at most one band (note that k∈K w k t = 0 corresponds with no deviation and is equivalent to w 0 t = 1). It is easy to observe that if the optimal value V of SEP is equal to 0, then (z,ȳ) is robust, since it is not possible to compromise the jamming of any TP for the given uncertainty set RHS-MB.…”

“…As pointed out by Commander et al in [12,13], though the problem is very relevant and there is a wide literature about preventing hostile jamming, surprisingly the NJP has been practically neglected until their work. Moreover, together with the work [14], these seem to be the only papers that have directly addressed the problem. Our main original contributions in the present paper are:…”

“…Our RO approach also presents a different way of dealing with uncertainty w.r.t. the scenario-based approach of [14]; 3. proposing an original robust cutting-plane algorithm to tackle the righthand-side (RHS) uncertainty coming from uncertain SIR quantities. Tackling RHS uncertainty by a canonical row-wise uncertainty approach and cardinality-constrained uncertainty sets like [6] leads to trivial and conservative robust counterparts.…”

Complexity Results for Wireless Sensor Network Scheduling

“…Depending on the application and the goal of researchers, several combinatorial optimization models and solution techniques have been proposed [1]. These models and problems include, among others, sensor localization and tracking [2][3][4][5], sensor scheduling [6][7][8][9][10][11] communication neutralization [12,13] and energy consumption [14,15]. There are a wide variety of methods and models because on one had several issues are considered in WSN.…”