Selfish node detection based on hierarchical game theory in IoT

EURASIP Journal on Wireless Communications and Networking

Table 3 Different metrics of the proposed methods in comparison with other methods

Presence of selfish node, algorithms	Metrics	10	15	20	25	30	35	40
Proposed algorithm	Detection accuracy (DA)	0.93	0.99	1	0.99	0.98	0.96	0.95
PPS [14]		0.75	0.72	0.78	0.79	0.77	0.76	0.74
Game theory-based [29]		0.81	0.79	0.71	0.69	0.56	0.51	0.48
Trust management [32]		1	0.95	0.88	0.85	0.8	0.74	0.62
Proposed algorithm	False-positive rate (FPR)	0	0	0	0.002	0.0127	0.0347	0.0549
PPS [14]		0.25	0.22	0.28	0.29	0.27	0.27	0.31
Game theory-based [29]		0.19	0.21	0.28	0.41	0.44	0.49	0.53
Trust management [32]		0	0	0	0.02	0.1	0.14	0.2
Proposed algorithm	Throughput	75.85	73.05	74.14	78.92	86.71	91.07	95
PPS [14]		48	41	43	38	35	32	19
Game theory-based [29]		50	42	41	36	33	30	15
Trust management [32]		85	78	81	84	82	71	73
Proposed algorithm	End-to-end delay (ms)	16.35	17.01	17	12	7.93	4.1	1.47
PPS [14]		48	41	43	38	35	32	19
Game theory-based [29]		50	42	41	36	33	30	15
Trust management [32]		85	78	81	84	82	71	73
Proposed Algorithm	Energy consumption (μJ), 2 CBR	3.14300	3.14302	3.14303	3.14299	3.14301	3.14301	3.14302
PPS [14]		3.1	3.25	4.2	4.8	4.9	5.01	5.2
Game theory-based [29]		2.8	2.9	3.5	3.6	3.66	3.78	3.9
Trust management [32]		2.9	3.1	3.4	3.9	4.12	4.52	4.6